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Cited by 391

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China

Dong, Kangyin, Sun, Renjin, Dong, Xiucheng·2018·Science of The Total Environment

Abstract

This study aims to test the environmental Kuznets curve (EKC) for carbon dioxide (CO2) emissions in China by developing a new framework based on the suggestion of Narayan and Narayan (2010). The dynamic effect of natural gas and renewable energy consumption on CO2 emissions is also analyzed. Considering the structural break observed in the sample, a series of econometric techniques allowing for structural breaks is utilized for the period 1965-2016. The empirical results confirm the existence of the EKC for CO2 emissions in China. Furthermore, in both the long-run and the short-run, the beneficial effects of natural gas and renewables on CO2 emission reduction are observable. In addition, the mitigation effect of natural gas on CO2 emissions will be weakened over time, while renewables will become progressively more important. Finally, policy suggestions are highlighted not only for mitigating CO2 emissions, but also for promoting growth in the natural gas and renewable energy industries.

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Metadata

Authors
Dong, K., Sun, R., & Dong, X.
Venue
Science of The Total Environment
Publisher
Elsevier BV
Published
2018-11
Volume
640-641
Pages
293-302
ISSN
0048-9697 (ISSN-L)
OpenAlex
W2805205542
Cited by
391
References
53
FWCI
60.56
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China. https://app.cochat.ai/papers/10.1016/j.scitotenv.2018.05.322
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Dong, K., Sun, R., & Dong, X. (2018). CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2018.05.322

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.